Download data from CA DFW Oil Spill Incident Tracking:
- https://map.dfg.ca.gov/metadata/ds0394.html
- https://gis.data.ca.gov/datasets/7464e3d6f4924b50ad06e5a553d71086_0/data
Load In Packages
Establish a new dataset based on specific parameters of the counties of California. Here, we will “read in” the data to isolate specific areas and their regional extent. This will provide a spatial basis that will be populated with designated points.
# Read in California county shapefile
# Shapefile will act as the polygon needed for visualization/population of data
# Data wrangle to manipulate data easily, isolate two attributes using select() and rename() for better recall
ca_counties <- read_sf(here("data", "ca_counties", "CA_Counties_TIGER2016.shp")) %>%
select(NAME, ALAND) %>%
rename(county_name = NAME, land_area = ALAND)
Read in the “oil spill” data. This dataset will overlay the map parameters we established earlier.
Exploratory interactive map
Use {tmap} to make an interactive map. We can explore the extent of the data, zoom in, and zoom out.
Where do we see the highest number of spills? To gain some more insight, let’s create a chloropleth map!
We can identify the density of oil spills per area. With similar data wrangling techniques, we can specify new paramters.
# Use st_join() to join the datasets to obtain counts by county
ca_oil_spill <- ca_counties %>%
st_join(oil_spill)
# Use counts() to find counts of incidents by county
ca_oil_spill_counts <- ca_oil_spill %>%
count(county_name)
Finalized chloropleth map
# Plot a chloropleth using the number of oil spill incidents as the fill color
ggplot(data = ca_oil_spill_counts) +
geom_sf(aes(fill = n), color = "white", size = 0.1) +
scale_fill_gradientn(colors = c("lightgray", "orange", "red")) +
theme_bw() +
labs(title = "CA inland oil spill incidents by county (2008)",
fill = "Number of oil spills")
